Deep learning for real-time crime forecasting and its ternarization

DOI10.1007/s11401-019-0168-yzbMath1453.91085arXiv1711.08833OpenAlexW2983165041WikidataQ126784973 ScholiaQ126784973MaRDI QIDQ2286236

Bao Wang, P. Jeffrey Brantingham, Penghang Yin, Jack X. Xin, Andrea L. Bertozzi, Stanley J. Osher

Publication date: 10 January 2020

Published in: Chinese Annals of Mathematics. Series B (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.08833

zbMATH Keywords

real-time forecastingcrime representationspatial-temporal deep learningternarization

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computational methods for problems pertaining to game theory, economics, and finance (91-08) Mathematical sociology (including anthropology) (91D99) Mathematical modeling or simulation for problems pertaining to game theory, economics, and finance (91-10)

Uses Software

• Theano
• cuDNN
• Adam
• BinaryConnect
• BinaryNet
• XNOR-Net
• LaplacianSmoothing-GradientDescent

Cites Work

• Gang rivalry dynamics via coupled point process networks
• A STATISTICAL MODEL OF CRIMINAL BEHAVIOR
• Nonlinear Patterns in Urban Crime: Hotspots, Bifurcations, and Suppression
• Self-Exciting Point Process Modeling of Crime

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